docs: update formatting of link

Author: xmatthias

docs/freqai-configuration.md

@@ -206,9 +206,9 @@ Regression and classification models differ in what targets they predict - a reg
 
 All of the aforementioned model libraries implement gradient boosted decision tree algorithms. They all work on the principle of ensemble learning, where predictions from multiple simple learners are combined to get a final prediction that is more stable and generalized. The simple learners in this case are decision trees. Gradient boosting refers to the method of learning, where each simple learner is built in sequence - the subsequent learner is used to improve on the error from the previous learner. If you want to learn more about the different model libraries you can find the information in their respective docs:
 
-* CatBoost: <https://catboost.ai/en/docs/> (No longer actively supported since 2025.12)
 * LightGBM: <https://lightgbm.readthedocs.io/en/v3.3.2/#>
-* XGBoost: https://xgboost.readthedocs.io/en/stable/#
+* XGBoost: <https://xgboost.readthedocs.io/en/stable/#>
+* CatBoost: <https://catboost.ai/en/docs/> (No longer actively supported since 2025.12)
 
 There are also numerous online articles describing and comparing the algorithms. Some relatively lightweight examples would be [CatBoost vs. LightGBM vs. XGBoost — Which is the best algorithm?](https://towardsdatascience.com/catboost-vs-lightgbm-vs-xgboost-c80f40662924#:~:text=In%20CatBoost%2C%20symmetric%20trees%2C%20or,the%20same%20depth%20can%20differ.) and [XGBoost, LightGBM or CatBoost — which boosting algorithm should I use?](https://medium.com/riskified-technology/xgboost-lightgbm-or-catboost-which-boosting-algorithm-should-i-use-e7fda7bb36bc). Keep in mind that the performance of each model is highly dependent on the application and so any reported metrics might not be true for your particular use of the model.